用数据驱动方法自动识别罕见样本并增强稀疏区域。
Regression Augmentation With Data-Driven Segmentation
- 基于马氏距离的GMM模型自动发现稀有样本
- 在32个基准数据集上优于现有最优增强方法
- 适合处理目标分布偏斜的回归任务
当目标分布存在偏斜时，回归任务会出现不平衡问题，导致模型只关注密集区域而忽略少数样本。尽管这一问题在众多应用中普遍存在，但针对此挑战的专门方法仍较少。现有方法通常依赖固定的、临时设定的阈值来区分稀有或常见样本，忽略了特征-目标空间的连续复杂性，无法准确刻画真正的稀疏区域。为此，我们提出一种完全数据驱动的GAN增强框架，利用马氏距离-高斯混合模型（Mahalanobis-Gaussian Mixture Modeling, GMM）自动识别少数样本，并采用确定性最近邻匹配来丰富稀疏区域。与预设阈值不同，本方法由数据本身决定哪些观测属于真正稀有。在32个基准不平衡回归数据集上的评估表明，该方法持续优于当前最先进的数据增强技术。
原文摘要 · Abstract (English)
Imbalanced regression arises when the target distribution is skewed, causing models to focus on dense regions and struggle with underrepresented (minority) samples. Despite its relevance across many applications, few methods have been designed specifically for this challenge. Existing approaches often rely on fixed, ad hoc thresholds to label samples as rare or common, overlooking the continuous complexity of the joint feature-target space and fail to represent the true underlying rare regions. To address these limitations, we propose a fully data-driven GAN-based augmentation framework that uses Mahalanobis-Gaussian Mixture Modeling (GMM) to automatically identify minority samples and employs deterministic nearest-neighbour matching to enrich sparse regions. Rather than preset thresholds, our method lets the data determine which observations are truly rare. Evaluation on 32 benchmark imbalanced regression datasets demonstrates that our approach consistently outperforms state-of-the-art data augmentation methods.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。